code: def __call__(self, input_image, hand=False):
      def to(tensor):
          tensor = tensor.to(self.device)
          tensor = tensor.half() if self.use_half else tensor.float()
          return tensor
  
      input_image = input_image[..., ::-1].copy()
      with torch.no_grad():
          candidate, subset = self.body_estimation(input_image, to)
          canvas = np.zeros_like(input_image)
          canvas = util.draw_bodypose(canvas, candidate, subset)
          if hand:
              hands_list = util.handDetect(
                  candidate, subset, input_image
              )
              all_hand_peaks = []
              for x, y, w, is_left in hands_list:
                  peaks = self.hand_estimation(
                      input_image[y : y + w, x : x + w, :], to
                  )
                  peaks[:, 0] = np.where(
                      peaks[:, 0] == 0, peaks[:, 0], peaks[:, 0] + x
                  )
                  peaks[:, 1] = np.where(
                      peaks[:, 1] == 0, peaks[:, 1], peaks[:, 1] + y
                  )
                  all_hand_peaks.append(peaks)
              canvas = util.draw_handpose(canvas, all_hand_peaks)
          return canvas, dict(
              candidate=candidate.tolist(), subset=subset.tolist()
          )
  
file_path: carefree-learn/cflearn/api/cv/third_party/openpose/api.py
imports: import os
  import torch
  import numpy
  from .None import util
  from .body import Body
  from .hand import Hand
  from .....misc.toolkit import download_model
label_desc: Use image node_id 3 for calling the OpenposeDetector obj's underlying member method code with example usage: obj.__call__(input_image, hand) and returns: tensor, canvas,  dict